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DOE OSTI2023

Differences In High Burnup Fuel Management Strategies to Minimize FFRD and Increase Economic Viability

The nuclear industry is pursuing approval of an increase in the length of the pressurized water reactor (PWR) cycle from 18 months to 24 months to reduce reactor downtime and enhance the economic competitiveness of nuclear energy. Such an increase in reactor cycle length will require that the maximum rod average burnup exceeds the current regulatory limit of 62 GWd/MTU, and it could peak at approximately 75 GWd/MTU, posing potential reactor safety and performance concerns. One such concern is that fuel fragmentation, relocation, and dispersal (FFRD) could occur during a severe loss-of coolant accident (LOCA) in which a fuel rod balloons and bursts, and pulverized fuel fragments are dispersed throughout the reactor’s primary coolant system. Previous analyses have identified which reactor operating conditions leave the core more susceptible to FFRD and have shown that FFRD susceptibility is strongly linked to fuel rod burnup and linear heat rate (LHR) history. The work described in this report uses an optimization strategy known as parallel simulated annealing (PSA) and a coarse mesh Purdue Advanced Reactor Core Simulator (PARCS) reactor physics model to develop two core fuel loading patterns, each with a different optimization objective. One core optimization maximized the core’s cycle length while still respecting regulatory limits on the radial peaking factor and soluble boron concentration with a peak rod average burnup of 75 GWd/MTU. The second optimization was aimed at minimizing FFRD susceptibility while still targeting a 24-month cycle length and respecting regulatory limits. PARCS model predictions were verified using the high-fidelity Virtual Environment for Reactor Applications (VERA). The two core designs were compared to highlight core design strategies to minimize FFRD susceptibility and to maximize economic viability.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Rapidly Viable Sustained Grid

Rapid recovery of power flow, possibly after a blackout, is a crucial need arising in scenarios that are increasingly becoming more frequent; here, solutions for rapid viability of power while the grid is being restored are urgently needed to keep critical infrastructure (CI) online. Increasingly, after the initial recovery phase, sustenance of reliable power requires assistive services to the grid for long periods of time. Even though the need is urgent, there is only sparse effort present toward a comprehensive framework/strategy for making power rapidly viable with an emphasis on sustained grid ancillary services; which is the focus of this proposal. The proposed concept envisions four phases. In the first phase, when a large portion of power is disrupted (see Figure 1(a)), emphasis is on bringing CI online with the objective of maximizing the time horizon of power viability using resources available at the CI. In the second phase (Figure 1(b)), neighborhood resources are tightly coordinated that forms the CI’s central-core (CC) to provide guaranteed viability of CI over a longer horizon. In the third phase, self-organizing power networks are expanded in a distributed layer supporting the central-core (Figure 1(c)). In the fourth phase, separate CI-networks coalesce and are controlled in a coordinated fashion to provide grid ancillary services (AS), such as primary frequency control and enhancing grid resiliency. With time, system efficiencies and penetration of renewables increase, while the time-horizon of guaranteed sustenance of CI is maximized. Under proposed work, the concept will be instantiated with a focus on medical centers as CI. Comprehensive power hardware in the loop strategies and emulated field tests will guide and validate devised solutions. A strong T2M effort to commercialize resulting technology is outlined. The proposed technology will be transformative for the grid. It will fundamentally change the way large contingencies are managed where power systems and critical infrastructure transition from being fragile to being robust using intelligent, self-organizing control for coordinating resources, enhanced resiliency and use of sustainable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Gamma Converter Box Preliminary Design [Slides]

Objective: Develop a test capability near Godiva IV fast burst reactor to maximize gamma dose to neutron dose ratio and gain increase in gamma dose over free field. Goal: Maximize gamma dose to neutron dose ratio and gain increase in gamma dose over free field.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Advanced CO 2 Capture Solvent Systems for Dynamic Power Generation

RTI International, in collaboration with Pacific Northwest National Laboratory (PNNL), Carbon Capture Simulation for Industry Impact (CCSI 2 ), Electricity Power Research Institute (EPRI), and West Virginia University (WVU), successfully completed a joint research effort in developing a cost-effective, resilient, load-following advanced CO 2 capture technology for natural gas power plants. The project’s objective was to develop a CO 2 capture process that maximizes the net present value (NPV) of the electricity sale by minimizing the levelized cost of electricity (LCOE) under dynamic plant loads and high renewable penetration environments. The two key innovations developed in this project were the use of (i) advanced water-lean solvents (WLSs) and (ii) process intensification equipment, such as a rotating packed bed (RPB) absorber and dual-stage flash regeneration. The process’s low CO 2 capture cost is realized through WLSs’ low energy required for solvent regeneration, which lowers the operating cost while RPBs intensify the absorption process and reduces the power plant capital cost. A suite of advanced computational and simulation packages was implemented to guide the process design, validate the dynamic response of the capture plant, evaluate system-wide performance, and maximize the power plant’s profit. The project also engaged with power producers and other stakeholders to ensure its technical relevance and techno-economic viability. The development of this highly disruptive CO 2 capture technology could accelerate the industry adoption and thereby lower the greenhouse gas emissions of the U.S. power sector. Deployment of this technology can increase the reliability and decrease the cost of electricity generation in the U.S. by enabling the use of low-carbon fossil fuels to balance fluctuations of renewable energy availability.

03 NATURAL GAS↗

DOE OSTI2024

Bid-in Price-Sensitive Load: A Comparison with Fixed Load in a Realistic Test System

Bulk power systems operations, in both simulation and practice, are often built on the principle of minimizing production costs, which assumes to first order that demand is fixed and that the only degrees of freedom for balancing generation and load are supply-side unit commitment and dispatch decisions. The purpose of this report is to interrogate the value of welfare-maximizing markets that not only physically balance demand and supply but also ensure that market outcomes provide consumer value by directly maximizing welfare, defined as the value of consumption minus production costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Distribution Network Capacity Market Design: Marginal Distribution Capacity Pricing Mechanism for Efficient Investment and Cost Allocation

As electricity markets begin to shift from reliance on large, centralized power plants and towards distributed energy resources (DERs), there is a growing acknowledgement that more efficient planning, operations, and oversight is needed in the distribution system. This paper addresses one step in that direction by proposing an auction mechanism that uses a detailed distribution system planning model that allocates permits to end-used customers who request capacity to install new devices at their location and network upgrade contracts to utilities or 3rd-party companies who offer to upgrade system components. The resulting plan maximizes market surplus, that is, maximizes the total benefit to consumers minus the cost of network upgrades. We apply a marginal pricing scheme to the auction’s results such that the cost of each permits or contracts is differentiated by time and location, based on the Lagrangian multipliers of binding network constraints. These prices are shown to be no greater than the bid price of any awarded contract and no less than the offered cost of any awarded upgrade contract. Furthermore, the nonlinearity of power flows in the planning model result in an additional surplus that would be collected by the entity that hosts the auction, which could then be refunded to market participants or used to cover overhead costs of running the market. We provide four example auction results in a simple three-node distribution feeder to demonstrate the properties of the design. Results suggest that larger or more realistic case studies could be a promising next step.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Achieving Cooperative Community Equitable Solar Sources (ACCESS) (Final Technical Report)

Since 2011, solar has grown from a niche technology to a widely accessible source of power for homes and businesses across the United States and has become a fundamental part of the modern grid. There are still challenges, however, in learning how to integrate and use PV most effectively and how to make PV universally available. Most low- and moderate-income (LMI) customers cannot currently afford PV; capital costs and financing costs are too high to drive significant penetration. Providing access to LMI individuals and communities is a critical and immediate priority, and the focus of this project. The overall objective of the Achieving Cooperative Community Equitable Solar Sources (ACCESS) project is to explore and amplify the use of innovative, cost-effective energy access programs to serve co-ops’ LMI members1. ACCESS will research at least three financing mechanisms and at least six LMI program designs including LMI engagement strategies to maximize participation for these hard-to-reach audiences. ACCESS evaluated the financial mechanisms and program designs from field tests sited at diverse co-ops that provide recognizable models for the broader co-op community to identify optimal solutions for small utilities. The research with these cooperatives allowed testing of concepts and development of models and tools for other utilities to adapt to their own program designs and expansions. ACCESS published results and developed an “ACCESS Solar Access Toolkit” consisting of program designs, LMI engagement strategies, how-to guidance, and other tools to facilitate replication at small utilities across the country. Through the dissemination practices of the ACCESS project team, all NRECA member co-ops (~900) were made aware of the “ACCESS Solar Access Toolkit” and all other ACCESS resources. NRECA and its partners developed innovations to expand co-ops’ solar energy offerings to provide all of a co-op’s members—especially those who struggle to pay their bills—with cost-effective options that meet their needs. ACCESS specifically explored utility financing mechanisms and program designs that, independently or used in combination, increase solar access for rural electric cooperatives’ LMI members/ratepayers and that reduce LMI member/ratepayers’ electricity costs by at least 10%. LMI engagement strategies focused on maximizing the number of members who receive benefits and on the cost savings to LMI participants.

14 SOLAR ENERGY↗

DOE OSTI2023

High-Throughput Directed Evolution of Marine Microalgae and Phototrophic Consortia for Improved Biomass Yields (Final Report)

Primary project achievements include using selective pressures (O 2 , light, temperature) and developing culturing regimes for the diatom Nitzschia inconspicua str. hildebrandi to attain enrichments with an ~90% increase in areal biomass productivity relative to the parental strain under pond-mimicking conditions with high O 2 stress in laboratory bioreactors. The resulting strain (GAI-337) was tested further for dilution time, culture density, CO 2 supplementation, pH, temperature, and dissolved O 2 concentration under outdoor pond-mimicking conditions to improve areal productivities. These experiments yielded an optimum harvest and dilution time just after sunset, ~0.45 g AFDW L -1 initial culture density for maximal productivities, no requirement for CO 2 supplementation or pH control, maximal performance under a diel temperature curve going from 24 °C at night to 36 °C during the day, and benefits from some O 2 removal from the culture by bubbling with air. Using pond-mimicking laboratory bioreactors, N. inconspicua GAI-337 achieved ~42 g AFDW m -2 d -1 . Nutrient limitation experiments resulted in a biomass composition that equated to ~160 Gallons of Gasoline Equivalent energy per ton AFDW, highlighting the potential of GAI-337 as a promising renewable fuel feedstock strain. Genome resequencing has revealed genome alterations potentially contributing to the improved growth of GAI-337 in the laboratory. Based on the comparative analyses of the GAI-337 and GAI-229 (reference) strains, we identified 144 single nucleotide substitutions that resulted in amino acid change, 7 single nucleotide substitutions that resulted in protein truncation; 5 deletions; and 1 frameshift mutation. From the mutations that potentially affect expression of functionally annotated genes, particular interest was noted for an interferon-induced 6-16 family protein that may be involved in the host immune response against microbe invasion; the chaperone protein DnaK, which may function to protect the folding of proteins within the cell; and SPRY domain protein that is found in many eukaryotic proteins important in cell signaling pathways. Transcriptome analysis revealed over 1000 genes with increased transcript levels. Many of these and many of the genes with mutations are not yet functionally annotated and an increased bioinformatics effort is necessary to more completely analyze the Nitzschia inconspicua genome. Adaptive laboratory evolution (ALE) was performed for over 300 days using consecutive 0.5°C temperature increases in a constant temperature incubator to attain greater thermal tolerance in Nitzschia inconspicua. The adapted strain was able to grow at a constant temperature of 37.5°C; whereas this constant temperature was lethal to the parental control, which had an upper temperature boundary of 35.5°C prior to adaptive evolution. Several high-temperature clonal isolates were obtained from the evolved population following ALE, and increased temperature tolerance was observed in clonal adapted cultures. The final temperature adaptation was maintained through cryopreservation and was observed in multiple clonal isolates, including multiple clonal isolates with significantly increased cell size, indicating the potential occurrence of a sexual cycle during the clonal isolation process. A survey of Nannochloropsis strains was conducted for tolerances to high pH and high bicarbonate media. Nannochloropsis granulata showed promising growth in diel bioreactors and was successfully grown at the GAI Kauai farm site in long-term growth campaigns. Co-culturing using Nitzschia inconspicua, Nannochloropsis and a cyanobacterium were assembled in the laboratory to determine if productivity synergies could be attained. Although all strains grew well in the laboratory high-bicarbonate media individually, the cyanobacterium quickly outgrew the other strains in the laboratory consortium pushing the co-culture away from a diverse (and potentially synergistic assemblage) phototroph culture towards a monoculture dominated by the cyanobacterium. Several outdoor growth campaigns were conducted, with productivities ranging between 10-20 g/m 2 /d of biomass. The best performing strain in the laboratory (GAI-337) did not outperform reference strains at the Kauai farm under the conditions used. Addition growth campaigns are necessary under conditions that result in higher biomass (>20 g/m 2 /d) and that attain higher O 2 levels are likely necessary. Initial data indicate that the thermally adapted strain did slightly better than the control strain at higher temperatures; however, additional campaigns are necessary to establish statistical significance. In summary, Nitzschia inconspicua is able to attain exemplary biomass and lipid yields in the laboratory bioreactors. Strain evolution to both O 2 and temperature resulted in targeted strain improvements. Additional outdoor campaigns are necessary to determine if laboratory improvements translate to the field.

09 BIOMASS FUELS↗

DOE OSTI2023

Optimization of Spirulina for biomanufacturing and the delivery of protein therapeutics

Lumen Bioscience has developed a novel cyanobacterial platform that enormously decreases the cost of pharmaceuticals used to prevent and treat illnesses. Arthrospira platensis (spirulina), is a photosynthetic microorganism that has been consumed as a dietary supplement for centuries in part because of its high protein content. Commercial cultivation operations have matured over the last 50 years to allow large-scale cultivation in outdoor ponds. Lumen recently discovered methods that transform this commercially important cyanobacterium into a genetically tractable platform for bioengineering. The high protein accumulation in spirulina makes it an ideal chassis for heterologous protein expression, and centuries of safe human consumption suggest its utility as an administration vehicle for biologic drugs. Lumen can express a broad range of recombinant proteins in spirulina and has developed manufacturing processes and strains to mucosally deliver bioactive proteins to treat and/or prevent disease. For example, Lumen has produced an oral cocktail of dried whole-cell spirulina biomass, containing 3 toxin-neutralizing antibodies and 1 endolysin, that is efficacious in a preclinical in vivo models of C. difficile infection. Lumen has also demonstrated that intranasal administration of a spirulina-manufactured neutralizing antibody can prevent disease by a respiratory pathogen, SARS-CoV-2, in a hamster model. Lumen’s current pipeline includes therapeutics to treat or prevent C. difficile infection, COVID-19, inflammatory bowel disease, cardiometabolic disease, and traveler’s diarrhea. Lumen deploys strategies to maximize the expression of these therapeutics for optimal dose sizing, a key limiting factor with past food crop-based expression systems. Spirulina can express exogenous proteins at unusually high levels (>15% of dry weight) but reaching this maximum value requires optimization of multiple interacting factors. We describe our current statistics design of experiments approach that optimizes these factors with minimal replicates. This maximizes exogenous protein expression allowing Lumen to generate spirulina-based therapeutics for a rapidly expanding range of medical uses.

Heinnickel, Mark↗

DOE OSTI2021

Production Facility Simulations for Annular LEU Targets

Eden Radioisotopes, LLC is designing a new molybdenum-99 (Mo-99) production facility based on a TRIGA-sized reactor with a core composed of low enriched uranium (LEU) targets. Reactor production is designed to exceed U.S. demand for Mo-99 using the fission processes. The nominal operational power is 1.8MW (less than 2.0MW, to satisfy the “non-power” reactor requirement) and desired irradiation scenarios include 7, 14, or 21 days of the irradiation for each target element. Safety limits for reactor operating margins are associated with such physical phenomena as fission gas release, fuel swelling, and stress cracking. As shown in the literature, one of the critical parameters defining safety margins for metallic uranium fuel is swelling. In its turn the fuel operational temperature is the main parameter impacting the swelling rate. This report documents the results of consecutive studies of reactor criticality, fission energy depositions, and cooling conditions for the target. The maximum temperature calculated for the LEU targets allows us to choose appropriate modeling approximation of the swelling. All obtained results refer to the preliminary targets and core designs provided by Eden. A criticality analysis was performed, and the results are listed mainly to cross-check Eden’s safety report and assume maximal neutron field disturbance for maximal reactivity. A brief description of the metallic uranium swelling model is provided in the Appendix.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Development of Low-Cost, High-Performance, Easy-To-Apply, Non-Flammable, Inorganic Phase Change Material (PCM) Technology (Project Final Report)

This report describes a 45-months long research program focused on the development of novel, easy-to-apply, non-flammable, and high-performance inorganic phase change materials (PCMs) for building and industrial applications. The University of Massachusetts Lowell (UML) formed a world-class team consisting of researchers form InsolCorp (only N. American manufacturer of inorganic PCM systems for building applications), and a group of industrial advisors, to develop a universal/multipurpose, simple-to-manufacture and cost-effective PCM technology. The project team expects that the results of this work will spur in the future the adoption of thermal storage materials – a key building energy saving technology as identified by DOE BTO – for a variety of building envelope applications. The main goal of this project was to demonstrate a suite of low-cost, multipurpose, and durable inorganic PCM formulations with phase transition temperatures encompassing typical building applications (between +5 o C and +55 o C). The first objective was to design, fabricate, and experimentally validate a performance of inexpensive, durable, highly efficient, non-flammable, and easy to manufacture PCMs. To allow a variety of building applications, the project team focused on formulations that exhibit repeatable phase transitions between +5 o C and +55 o C. To follow the DOE BTO cost efficiency target without compromising thermal performance, our work was based on inorganic compounds (mostly salt hydrates) and their blends, which represent a fraction of the cost of most of organic PCMs with about twice as high density as well as significantly higher thermal conductivity and phase change enthalpy. The second objective was to develop easy-to-manufacture and -install packaging/encapsulation designs that are 1) a superior barrier to current state-of-the-art macro-packaging, which significantly reduces the risk of loss of hydration water and PCM leak, and 2) optimal in enhancing the heat exchange rates with the surroundings and within the PCM core to ensure complete charging/discharging of the entire PCM within the product. Finally, the project’s intend was to scale-up the fabrication process to demonstrate installation on system-scale applications, and to validate the performance under field conditions. This work aimed at developing low-cost, high-energy storage, and reliable latent heat storage technology for building applications. This development was realized by formulating and integrating the following two technology components: 1) inorganic salt hydrate based PCMs that have high latent enthalpies and are low-cost and durable, and 2) PCM encapsulation (packaging) technology that maximizes PCM concentration and enhances heat transport characteristics in the product and with the external environment/materials. High thermal storage capacity, low cost and fire resistance are key to the building market entry for PCM technology. Therefore, the project’s focus was on salt-hydrate-based formulations which satisfy all these criteria. Packaging and/or encapsulation of PCM is a key processing step. The project team recognized that a low-cost and simple-to-manufacture salt hydrate-based PCM technology holds the best chance to be successful in the building construction market, a market which is traditionally extremely sensitive to cost and where commodity thermal insulations are the benchmark for envelope-related energy saving measures. That is why, in this project, the main intention was to minimize the production cost and maximize the product energy storage density without sacrificing the PCM performance. It was achieved through: 1. Minimizing the non-PCM components (plastics, additives, packaging/encapsulation materials, etc.) because they are significantly more expensive than salt hydrates, 2. Using highly thermally conductive and lightweight PCM carrier (packaging material) to facilitate more complete phase cycling, and 3. Optimizing the thickness and minimizing air spaces in product design (such as in pouched PCM). For this purpose, our approach was to enable an easy system design, including selection of the PCM operating temperatures, optimizing the necessary heat storage capacity (by stacking together several layers of PCM products), and if needed, a synchronized usage of PCM products of different temperatures. A specially designed, robust, highly thermally conducting and highly impermeable packaging (to retain salt hydrate water during phase transition cycles) was designed and tested to increase the overall system thermal performance and durability. All PCM products developed during this project were tested in both lab scale and in full scale field conditions. It is expected that, after further developments and commercialization, the developed PCM technologies may be also applied in space conditioning, energy storage technologies, and heat transfer applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Non-Refrigerated Warehouse Design Guide [Slides]

Warehouses typically have a low energy use relative to their floor area. This make warehouses ideal to install solar panels and offset on-site energy use and have a surplus of energy if roof area is maximized. This surplus can be used to provide things like grid services or charge electric vehicle fleets. This document is intended to provide owners and operators of warehouse information on designing efficient/decarbonized warehouses and maximize on-site generation. The information here references outside publications to provide specific guidance, such as the ASHRAE Advanced Energy Design Guides, but tailors the information specifically to warehouses. This guide provides owners a better understanding of warehouse design as well as more specific information for themselves or outside designers to reference.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Woody Feedstock 2022 State of Technology Report

The U.S. Department of Energy promotes production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement in this mission, Idaho National Laboratory completes an annual SOT report for n th -plant and 1 st -plant woody biomass feedstock logistics. The purpose of the SOT is to provide the status of feedstock supply system technology development for woody biomass to biofuels relative to technical targets and cost goals from specific design cases, based on data and experimental results. Conventional feedstock supply systems need to be modified to meet the demands of conversion pathways, specifically to have the ability to adjust the quality of the raw biomass materials. Advanced systems incorporate innovative methods of material handling, preprocessing and supply chain configuration. In advanced designs, variability of the raw biomass can be reduced to produce feedstocks of a uniform format, moving toward biomass commoditization. Against this backdrop, the 2022 Woody SOT for low-ash woody feedstocks utilizes feedstock fractionation by incorporating technologies that can separate the biomass into its anatomical fractions (wood, bark, needle, and extrinsic ash) to reduce impurities and attempt to maximize the retention of usable fractions that satisfy downstream quality considerations. By using a series of air classification steps, this strategy can reduce the extrinsic ash in forest residues, separate out a majority of the incoming needles (which can be supplied to alternate markets), and maximize the retention of whitewood in the usable fraction. The fractionated forest residues are then mixed with clean-pine chips in a 50-50 blend to prepare the feedstock for the desired conversion pathway. The n th -plant analysis estimated the delivered cost for the feedstock at $\$$69.23/dry ton (2016$\$$) which represents a $\$$6.64/dry ton decrease compared to the cost estimate of the 2021 Woody SOT supply system for low-ash woody feedstocks. The quality requirements in the 2022 Woody SOT were identical to those of the 2021 Woody SOT at = 1.00 wt % ash and = 50.51 wt% carbon. The cost savings derive primarily from reductions in dry matter losses during air classification. The GHG emissions for the n th -plant analysis were estimated at 178.39 kg CO2e/dry ton compared to 178.71 kg CO 2 e/dry ton in the 2021 Woody SOT, a decrease of 0.32 kg CO2e/dry ton. The small change stems from an increase in emissions attributed to preprocessing and slightly larger savings in emissions from transportation. In the 1 st -plant analysis of the 2022 Woody SOT system, the average throughput was estimated to be approximately 2,128 dry tons/day or 96.51% of the name plate capacity. During the simulation the daily throughput ranged from 1,090 dry tons/day to 2,200 dry tons/day, or 49.43% to 99.75% of the daily nameplate capacity. After the year of operation 722,403 tons of processed feedstock were produced in total without regard to quality considerations (99.64% of the annual nameplate capacity). The variability in throughput was primarily caused by equipment failures in the system. Regular failures, downtime caused by routine maintenance per manufacturer guidelines, contributed to a majority 62.50% of failures and 62.60% of downtime. Failures due to wear were the other cause of disruption within the system, impacting the rotary shear and orbital screen and accounting for 37.50% of the failures and 37.40% of the total downtime. Ultimately the system was on stream for 87.84% during the simulation period, which is only 2.16 percentage points below the nth-plant assumption for on-stream time. The production cost of the system averaged $\$$71.66/dry ton. The costs ranged from a minimum of $\$$71.23/dry ton to a maximum of $\$$2,115.30/dry ton. When dry matter losses (disposed low-quality fractions as well as other losses such as in grinders) were considered the costs increased to an average of $\$$75.11/dry ton with a minimum of $\$$74.69/dry ton and a maximum of $\$$2,136.86/dry ton...

09 BIOMASS FUELS↗

DOE OSTI2024

A Model for Optimally Allocating Curbside Space Among Competing Uses

The emergence of various new forms of urban mobility services in recent years is leading to new pressures on curbside space. Municipalities, the entities typically responsible for managing the curbside, are in many instances handling these growing pressures by reallocating portions of the curbside away from traditional uses (such as metered and residential parking) in favor of uses such as ridehailing, scooter and bike-share corrals. As yet, however, such actions are being undertaken on an ad-hoc basis, due to the rapidly growing complexity of the curbside and the lack of standard analytical approaches. This lack of analytical capability is due to the traditional focus of transportation network modeling being focused predominantly on the interaction of supply and demand on links and nodes, with limited focus on link edges (the curbside). In this paper we address this research need by proposing a framework for modeling inter-modal competition for curbside space, inspired by the classical Bid-Rent Model of urban land use, intended to support curb managers to move towards maximizing the aspects of economic welfare that relate to curb access. In the bi-level model, choices made by the curbside manager impact travelers’ mode choices, and vice versa. We then present a simple numerical case study to demonstrate the properties of the proposed model, showing its tractability, flexibility, and intuitive sensitivity to systematic variation in inputs. The framework demonstrates the type of adaptive and evolving approach needed to maximize benefits from increasingly dynamic curb management strategies. The paper concludes with a brief discussion of future research needs to advance this line of inquiry.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Connecting Nitrogen Transformations Mediated by the Rhizosphere Microbiome to Perennial Cropping System Productivity in Marginal Lands

The demand for energy from biofuel production is increasing, prompting concerns about the environmental impact and long-term sustainability of bioenergy cropping systems. These cropping systems will make up much of our future landscapes, and threaten to take the place of food cropping systems. Many life cycle analyses of bioenergy sustainability focus on carbon accrual and budgets, since they want to maximize carbon accrual while producing alternative fuel. Less attention has been given to nitrogen (N) dynamics in these systems. N is the most commonly limiting nutrient for plants, but applying nitrogen fertilizer- as we do for most cropping systems – is harmful to the environment, energetically costly, and produces greenhouse gases. In other words, adding nitrogen by fertilizer bioenergy systems could add to the very problems (climate change) it is trying to address. This is especially true for the areas that are proposed for bioenergy systems: marginal lands. These more degraded lands do not complete with food crops, but do have limited nitrogen. If we are to use these marginal lands for bioenergy, we need to understand the mechanisms regulating nutrient acquisition, and identify ways that bioenergy crops can get nitrogen in sustainable ways. Nutrient acquisition in the soil is performed by microbes in the root zone, or rhizosphere. Microbes can either mineralize nitrogen in the soil (from organic forms) or fix nitrogen from the air, in a process called nitrogen fixation. The goal of our project was thus to understand how the rhizosphere microbiome provides nutrients to bioenergy crops on marginal lands. We focus especially on the process of nitrogen fixation, since it has potential to get “fertilizer for free” that has much less environmental harm. We investigated this goal using sites from the DOE Great Lakes Bioenergy Research Center (GLBRC) in the upper Midwest, and associated lab and ‘omics methods. We group our findings into three major areas. First, we showed that nitrogen fixation, the conversion of N2 gas from the air to ammonium that is usable by plants, is performed in bioenergy soils, and benefits switchgrass crops. While more well-studied in leguminous plants, free-living nitrogen fixation can occur in some systems, and represents a potential opportunity to gain ‘free’ sustainable nitrogen source. We identified the nitrogen fixing bacteria that were most active in providing switchgrass with N, and showed that the drivers of nitrogen fixation occurred at a microscale; it is not well-predicted by bulk variables like soil moisture or plant phenology. Second, we showed that nitrogen fixation is not suppressed by long-term fertilizer. We expected that plentiful nitrogen would reduce the symbiotic relationship between nitrogen fixers and plants, and ‘downregulate’ fixation. We did not find evidence for this, either after long-term fertilizer in the field, or short-term fertilizer in the greenhouse. Finally, we identified the root exudates, carbon compounds that are emitted from the root, that best stimulate nitrogen fixation. We found that carbohydrates were better at stimulating fixation than organic acids. We expected these exudates to be emitted from the plant in periods of high N demand, but we found they are emitted when N is plentiful. This suggests that the stimulation of N fixation by plants is a passive process. Overall, we show that nitrogen fixation has potential to support bioenergy cropping system, and future management could develop ways to maximize it. However, this may not be best achieved via the plant – we found very little evidence of a ‘transactional’ system by which plants are controlling when and where nitrogen fixation is stimulated. It will be better to understand how management practices like planting and fertilizer application affect the microscale soil dynamics, which will determine nitrogen fixation rates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Integrated Spatial, Spectral, & Temporal Optical Reflectance System for Precision Occupancy & Location Sensing to Improve Building Energy Efficiency

Buildings consume approximately 35% of the electricity used in the U.S. and building owners can significantly reduce this energy use by providing services like heating, electrical power and lighting only when people are present. The ARPAe funded program titled “INTEGRATED SPATIAL, SPECTRAL, & TEMPORAL OPTICAL REFLECTANCE SYSTEM FOR PRECISION OCCUPANCY & LOCATION SENSING TO IMPROVE BUILDING ENERGY EFFICIENCY” demonstrates how a low cost sensor technology developed for measuring distances can be used to count and locate occupants with a high degree of precision with a very low error rates. This platform tells a building control system where occupants are located (but not who they are) so that energy consuming services can be provided only when the services are needed by building occupants. The original proof of concept involved using low cost, commercially available time-of-flight (TOF) sensors that measure distance, but the performance of these existing sensors was lacking, as they could not operate properly in the presence of sunlight, which blinded the simple TOF sensors and limited their utility in buildings. This project proposed a powerful new class of TOF sensors that used state-of-the-art integrated circuit (IC) fabrication processes that combined advanced photonics with conventional silicon chip circuitry for improved sensor performance. An equally important part of this project was to find ways to maximize occupant count and location accuracy while using the fewest number of sensors possible, in order to keep costs low. By using building blueprints to create digital twins of commercial building spaces, the team developed new algorithms to maximize occupant count and tracking accuracy by properly locating the minimum number of sensors at just the right spots in the building. This capability not only minimizes system costs but also simplified sensor installation and system commissioning. Our simulations of our sensor networks for a range of commercial floorplan designs demonstrated that our installed cost target of $0.08/sqft was attainable, though not fully demonstrated during the project. Finally, we noted that the TOF sensor concept could provide a valuable role in health and eldercare by tracking patients without the need for worn sensors and would be useful for fall detection and other patient safety metrics, including tracking healthcare/patient interactions. We feel that, when fully developed, this new class of sophisticated TOF sensors and support software will be a powerful new approach to improving building energy efficiency based on occupant centric control platforms and will also open new levels of patient safety in healthcare operations. To realize this potential, the team formed the Troy Sensor Company LLC to oversee licensing of the programs patents and continue to seek commercialization of this program’s activity sensing technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗